class_docstr: Compute node embeddings for incoming and outgoing directions
  separately, and then concatenate the two output node embeddings
  after the final layer.
  .. math::
     h_{i}^{0} = [ x_i \| \mathbf{0} ]
  
     a_{i, \vdash}^{t} = \sum_{j\in\mathcal{N}_{\vdash }(i)} W_{\vdash} h_{j, \vdash}^{t}
  
     a_{i, \dashv}^{t} = \sum_{j\in\mathcal{N}_{\dashv }(i)} W_{\dashv} h_{j, \dashv}^{t}
  
     h_{i, \vdash}^{t+1} = \mathrm{GRU}_{\vdash}(a_{i, \vdash}^{t}, h_{i, \vdash}^{t})
  
     h_{i, \dashv}^{t+1} = \mathrm{GRU}_{\dashv}(a_{i, \dashv}^{t}, h_{i, \dashv}^{t})
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
  Attributes
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
docstr: Reinitialize learnable parameters.
docstr_tok: ["Reinitialize","learnable","parameters","."]
code: def reset_parameters(self):
      """Reinitialize learnable parameters."""
      gain = nn.init.calculate_gain("relu")
      self.gru_in.reset_parameters()
      self.gru_out.reset_parameters()
  
      for linear in self.linears_in:
          nn.init.xavier_normal_(linear.weight, gain=gain)
          nn.init.zeros_(linear.bias)
  
      for linear in self.linears_out:
          nn.init.xavier_normal_(linear.weight, gain=gain)
          nn.init.zeros_(linear.bias)
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  from torch.nn import init
  from ...data.data import GraphData
  from .base import GNNBase, GNNLayerBase
label_desc: Use image node_id 2 for calling the BiSepGGNNLayerConv obj's underlying member method code with example usage: obj.reset_parameters() without return types